Apparatus for detecting sleep apnea using electrocardiogram signals

ABSTRACT

An apparatus for diagnosing sleep apnea only uses an electrocardiogram signal and a computer processor and associated computer code that are configured to analyze the electrocardiogram signal and classify each time period of the electrocardiogram signal as either apneic or normal. A diagnostic measure of sleep apnea for the human patient is provided based on classification results obtained by combining a results for various electrocardiogram signal time periods. A computer-readable medium has computer code that causes the computer to analyze an electrocardiogram signal relating to the human patient and classify each time period in a set of time periods as either apneic or normal. A diagnostic measure of sleep apnea is provided based on classification results obtained by combining a plurality of results from the set of time periods using either time or frequency domain processing, or both.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation under 35 U.S.C. §120 of applicationSer. No. 09/952,688 filed on Sep. 14, 2001, the entire contents of whichare incorporated herein by reference.

BACKGROUND

This disclosure relates to cardio-respiratory monitoring and analysis,and more particularly to methods for diagnosing sleep disorders. Morespecifically, this disclosure is aimed at detection of sleep apnea usingthe electrocardiogram. The invention can be embodied in a form suitablefor use in a dedicated medical setting, or in the home.

Sleep apnea is a significant public health problem. Current estimatesare that approximately 4% of the male middle-aged population, and 2% ofthe female middle-aged population suffer from sleep apnea. Patientssuffering from sleep apnea are more prone to hypertension, heartdisease, stroke, and irregular heart rhythms. Continued interruption ofquality sleep is also associated with depression, irritability, loss ofmemory, lack of energy, and a higher risk of car and workplaceaccidents.

Current techniques for detection and diagnosis of sleep apnea rely uponhospital-based polysomnography. A polysomnogram simultaneously recordsmultiple physiologic signals from the sleeping patient. A typicalpolysomnogram includes measurements of blood oxygen saturation level,blood pressure, electroencephalogram, electrocardiogram,electrooculogram, electromyogram, nasal and/or oral airflow chesteffort, and abdominal effort. Typically, signals are recorded from afull night's sleep and then a diagnosis is reached following a clinicalreview of recorded signals. In some patients a second night's recordingis required. Because of the number and variety of measurements made,this test can be uncomfortable for the patient and also has a relativelyhigh cost. In general, it is only performed in a dedicated medicalfacility.

A variety of techniques have been proposed for simpler systems to detectsleep apnea. Acoustic screening devices have been proposed which detectloud snoring, or which detect long periods of silence which may indicatea dangerously long acute sleep apnea episode.

These are disclosed in U.S. Pat. No. 4,715,367. Other acoustic-baseddevices are disclosed in U.S. Pat. Nos. 5,797,852, 4,306,567, 4,129,125,and United Kingdom Patent Specification No. 2,214,302.

Detection systems using only measurements of respiratory effort or flowhave also been disclosed. U.S. Pat. No. 6,062,216 discloses the use of alight beam to detect breathing motion. U.S. Pat. No. 6,142,950 uses anairflow sensor attached to the upper lip to detect inspiration andexpiration airflow. WO 99/34864 discloses a nasal thermistor whichresponds to changes in nasal airflow and hence provides assessment ofapnea.

A variety of detection systems have been disclosed which usecombinations of measurements to detect sleep apnea. U.S. Pat. No.6,091,973 uses measurements of skin conductance, heart rate, and bloodoxygen saturation to detect arousals from apnea or hypopnea. U.S. Pat.No. 5,769,084 relies on processing of combinations of nasal air-flow,chest wall effort, oxygen saturation, heart rate, and heart activity inorder to identify the onset and duration of breathing disorder. U.S.Pat. No. 5,765,563 discloses a system for using measurements of airflow,heart rate, and oxygen saturation for detection of apnea, hypopnea, andoxygen desaturation. U.S. Pat. No. 5,275,159 discloses a system whichcombines heart rate, respiratory and snoring sounds, oxygen saturation,and bodily position to detect apnea. U.S. Pat. No. 5,105,354 discloses asystem for combining respiration and heart rate to detect sleep apnea ininfants. U.S. Pat. No. 4,982,738 discloses a system which combines heartrate, respiratory and snoring sounds to detect apnea. U.S. Pat. No.5,291,400 incorporates a system for the analysis of heart ratevariability, but not in relation to the detection of sleep apnea.

Japanese Patent Specification No. JP 5,200,001 discloses a technique formeasuring chest wall motion and hence detecting apnea. U.S. Pat. No.5,891,023 discloses a technique for using desaturation and resaturationevents in oxygen saturation.

Disadvantages of solutions based on the prior art include a high levelof complexity due to the number of measurements required, and/orrelatively low levels of accuracy in correctly diagnosing sleep apnea.The present invention seeks to overcome the aforementioned disadvantagesassociated with the prior art

Review of Electrocardiogram Terminology

The Electrocardiogram:

The heart is a muscular organ containing four chambers. The two smallestchambers are the left and right atria and the two largest chambers areleft and right ventricles. The heart alternatively contracts and relaxesat the rate of approximately once per second as it pumps blood aroundthe body. During this cycle (or beat) there are changes to theelectrical charge surrounding the heart cells that result in potentialgradients on the body surface. Any two electrodes placed on the bodysurface can measure these potential gradients. The electrocardiogramsignal is a plot of these body surface potential differences againsttime. Thus, the electrocardiogram is a non-invasive technique formeasuring the electrical activity (or cardiac potentials) of the heart.

Normal Electrocardiogram

The normal electrocardiogram has a number of characteristic patternsassociated with each beat of the heart: the P wave, the QRS complex andthe T wave. A number of measurements are routinely measured from theelectrocardiogram relating to various inter- and intra-beatcharacteristics. An example of an inter-beat measurement is the timeduration between each R wave peak of the QRS complex, referred to hereinas the RR interval. Examples of intra-beat measurements include PR andQT intervals. A typical electrocardiogram signal obtained from astandard lead configuration is shown in FIG. 1 and consists of the threestandard waveform components. The PR interval and the QT interval areidentified on FIG. 1 as well as the PR segment and the ST segment. Theletters do not have physiologic significance but the corresponding wavesdo as they relate to the electrical activity in specific regions of theheart.

P Wave:

During a beat of the heart the first event normally visible on theelectrocardiogram is the P wave. The P wave occurs as a result of theelectrical activity associated with the contraction of the two atria. Insome electrocardiograms the P wave may not be visible. The normalduration of the P wave is 0 (no visible P wave) to 100 ms, measured fromthe onset to the offset of the P wave.

QRS Complex:

The next event apparent on the electrocardiogram is the QRS complex,which results from the electrical activity associated with contractionof the two ventricles. A normal QRS complex is generally comprised of aQ wave, an R wave and an S wave. Every positive deflection in thiscomplex is called an R wave. The first negative deflection prior to theR wave is termed a Q wave and the first negative deflection followingthe R wave is called an S wave. Second and third positive deflectionsare possible and are called an R′ wave and an R″ wave respectively. Theinitial part of the QRS complex is related to the activity of bothventricles and the latter part is principally the left ventricle. TheQRS complex is a much larger signal than the P wave for two reasons.Firstly, the ventricles are closer to the chest surface than the atriaand secondly the ventricles contain much more tissue than the atria.

The QRS duration is measured from the start of the Q wave to the end ofthe S wave. It represents the amount of time needed for ventriculardepolarization and its normal duration is 50-100 ms.

T Wave:

The last major event of the electrocardiogram it the T wave and itcorresponds to the electrical activity associated with the ventriclesrelaxing. The normal T wave duration is typically between 100-250 ms.The atria also have a relaxation phase. This is not visible on theelectrocardiogram as it occurs at the same time as the much larger QRScomplex.

SUMMARY

This disclosure provides diagnostic recording apparatus including meansfor measuring cardiac potentials from the skin. The apparatus of theinvention also includes signal processing techniques to filter outunwanted interference due to motion artifact, electromyograms, powerline noise, and baseline wander. It incorporates means for recordingboth the unprocessed and processed cardiac potential signals. Thisdiagnostic recording apparatus is capable of communicating with externaldevices (e.g., computer, mobile communication terminal, stand-aloneconsole) using either direct physical connection or wireless connection.In an alternative embodiment, the apparatus may incorporate its owndisplay interface that allows direct inspection of the analysis results.

Accordingly, this disclosure provides a diagnostic recording apparatuscomprising: means for measuring cardiac electrical potential from ahuman for generating an electrocardiogram signal; means for analyzingsaid electrocardiogram signal to produce an output signal; and means forproviding a diagnostic measure of sleep apnea based on said outputsignal.

The apparatus may include signal processing means for filtering outunwanted interference from the electrocardiogram signal and forproducing a processed electrocardiogram signal for inputting to theanalyzing means.

The apparatus may also include means for recording the processedelectrocardiogram signal.

The means for analyzing said processed electrocardiogram signal maycomprise a computer algorithm performed within said apparatus.Alternatively, said means may comprise a computer algorithm performed onan external device, and the apparatus includes means for communicatingwith said device. Preferably, the apparatus includes a display interfacewhich allows direct inspection of the analysis results.

Only a single channel of electrocardiogram signal may be analyzed.Alternatively, a multichannel signal may be employed.

In one arrangement the apparatus includes; means for calculating the RRtime interval between successive QRS complexes from theelectrocardiogram signal; means for isolating anelectrocardiogram-derived respiratory signal; and means for obtainingmeasurements from the RR intervals and electrocardiogram-derivedrespiratory signal to provide a diagnostic measure.

In a second aspect, this disclosure provides a method of obtaining adiagnostic measure of sleep apnea, the method comprising a. acquiringsingle-channel or multi-channel electrocardiogram signal from a humanover a period of time; b. filtering the signal to remove electricalinterference; c. calculating a sequence of RR intervals from theelectrocardiogram signal by measuring the time interval betweensuccessive QRS complexes; d. obtaining an electrocardiogram-derivedrespiratory signal; e. partitioning the electrocardiogram signal into aset of shorter time periods; f. obtaining measurements from the sequenceof RR intervals and the electrocardiogram-derived respiratory signal foreach time period; g. for each time period, calculating the probabilitythat it can be classified as apneic or normal by processing themeasurements for that time period using a classifier model which hasbeen trained on a pre-existing database of signals; and h. combiningprobabilities for each time period to provide an overall diagnosticmeasure.

The measurements in step (f) may be selected from the group consistingof: I. the power spectral density of the electrocardiogram-derivedrespiratory signal; II. the mean and standard deviation of theelectrocardiogram-derive—d respiratory signal; III. the power spectraldensity of the RR intervals; IV. the mean and standard deviation of theRR intervals; V. the first five serial correlation coefficients of theRR intervals; VI. the Allan factor A(T) evaluated at a time scale T of5, 10, 15, 20 and 25 seconds where the Allan factor is defined as${{A(T)} = \frac{E\left\{ \left\lbrack {{N_{i + 1}(T)} - {N_{i}(T)}} \right\rbrack^{2} \right\}}{2E\left\{ {N_{i + 1}(T)} \right\}}},$where N/(T) is the number of R wave peaks occurring in a window oflength T stretching from iT to (i+1)T, and E is the expectationoperator; VII. the NN50 measure (variant 1), defined as number of pairsof adjacent RR intervals where the first interval exceeds the secondinterval by more than 50 ms; VIII. the NN50 measure (variant 2), definedas number of pairs of adjacent RR intervals where the second intervalexceeds the first interval by more than 50 ms; IX. two pNN50 measures,defined as each NN50 measure divided by the total number of RR intervalsX. the SDSD measures, defined as the standard deviation of thedifferences between adjacent RR intervals, and XI. the RMSSD measuredefined as the square root of the mean of the sum of the squares ofdifferences between adjacent RR intervals; XII. the mean or standarddeviation of the RR signal over the entire recorded electrocardiogram;and XIII. the mean or standard deviation of theelectrocardiogram-derived respiratory signal over the entire recordedelectrocardiogram.

In step (c) the RR interval sequence is obtained by first identifyingQRS complexes in the electrocardiogram signal and determining the timesof occurrences of the R-wave peak in the QRS complexes thereby providinga series of times at which the R-wave peaks occur. The sequence of RRintervals is then obtained by measuring the time interval betweensuccessive R-wave peaks.

The electrocardiogram-derived respiratory signal in step (d) is obtainedby extracting QRS complexes in the electrocardiogram signal anddetermining the times of occurrences of the R-wave peak in the QRScomplexes and by taking the locations of the R-wave peaks andcalculating the area represented by the signal near said locations.After step (g), the method further includes the step of recalculatingthe probability of a given time period containing an apneic episodeusing the probabilities generated for neighboring time periodscalculated in step (g).

The method includes the step of converting the diagnostic measure to aApnea-Hyponea Index or Respiratory Disturbance Index.

In one arrangement, after step (b), the method also includes determiningwhether the electrocardiogram signal is suitable for proceeding tocarrying out the analysis; and indicating if the electrocardiogramsignal is deemed unsuitable following said determination step.

The power spectral density of the electrocardiogram-derived respiratorysignal is calculated using an averaged periodogram technique. The powerspectral density of the RR intervals is calculated using an averagedperiodogram technique

The classifier model may be selected from the group consisting of: (1) alinear discriminant classifier; (2) a quadratic discriminant classifier;and (3) a regularized quadratic discriminant classifier.

According to yet another aspect, this disclosure provides a system forproviding a diagnostic measure for sleep apnea including a diagnosticrecording apparatus including means for measuring cardiac potentialsfrom a human to generate an unprocessed electrocardiogram signal; meansfor processing the unprocessed electrocardiogram signal and extractingRR intervals and an electrocardiogram-derived respiratory signal fromthe unprocessed electrocardiogram signal, where RR intervals are definedas the time durations between successive QRS complexes in theelectrocardiogram signal; means for dividing the electrocardiogramsignal into time periods; means for identifying time periods ascontaining apneic episodes and time periods not containing apneicepisodes; means for providing reports which indicate apneic periods,non-apneic periods and indeterminate periods, and for correlating theseevents with the electrocardiogram signal; and means for providing anoverall diagnostic measure in a clinically meaningful form based on thereports.

Means are provided for correlating the apneic, non-apneic andindeterminate periods with the electrocardiogram signal using a visualrepresentation.

According to yet a further aspect, this disclosure provides a method ofobtaining a diagnostic measure of sleep apnea, comprising measuringcardiac electrical potential from the skin of a patient; generating anelectrocardiogram signal from the measured potential; analyzing theelectrocardiogram signal to produce an output signal; and providing adiagnostic measure of sleep apnea based on said output signal.

Conveniently, the method includes processing the electrocardiogramsignal to filter out unwanted interference therefrom and analysing theprocessed signal. The diagnostic summary is provided in units of apneicminutes/hour, Apnea-Hypopnea Index, or other such clinically meaningfulform.

A method of diagnosing sleep apnea includes computing measurements basedon the RR interval power spectral density, the electrocardiogram-derivedrespiratory signal power spectral density, the mean and standarddeviation of the RR signal and the electrocardiogram-derived respiratorysignal, and a range of time-domain measurements of RR variability. Thesemeasurements are processed by a classifier which has been trained on apre-existing data base of electrocardiogram signals, and which providesa probability of a specific time period containing apneic episodes. Theprobability for a number of time periods can be combined to form theoverall diagnostic summary for a patient.

Thus, the present invention provides a diagnostic recording apparatuscomprising means for measuring cardiac electrical potentials from humanskin, signal processing techniques for filtering out unwantedinterference, giving a processed cardiac potential signal, means forrecording either or both the unprocessed and processed cardiac potentialsignals, means for analyzing said cardiac potential signals and meansfor providing a diagnosis of sleep apnea using only saidelectrocardiogram signal.

One aspect of the apparatus may include means for communicating withexternal devices for analyzing the recorded electrocardiogram signal.

Ideally, the means for analyzing said recorded cardiac potential signalcomprises a computer algorithm performed on a computer located remotelyfrom the diagnostic recording apparatus. The apparatus may communicatewith the computer via wireless or wire connection.

Advantageously, the means for analyzing said recorded cardiac potentialsignal comprises a computer algorithm performed within said apparatus sothat the apparatus comprises means for recording the electrocardiogramsignal and means for carrying out analysis of said sleep apnea usingonly a single channel of electrocardiogram signal.

In another aspect, the apparatus includes means for calculatingintervals between R wave peaks on the electrocardiogram signal, therebyproviding a sequence of RR intervals and means for isolating anelectrocardiogram-derived respiratory signal. Means are provided forusing spectral measures derived from the RR intervals andelectrocardiogram-derived respiratory signal.

In one arrangement, the recording apparatus includes its own displayinterface which allows direct inspection of the analysis results.

The method may include the step of incorporating additional processingof the classifications of neighboring time periods to increase theaccuracy of a given time period.

In one aspect, the method may include converting the diagnosis on aper-minute basis to the Apnea-Hyponea index or Respiratory DisturbanceIndex.

The electrocardiogram-derived respiratory signal may be calculated bytaking the locations of the R-wave peaks and calculating the areaenclosed by the signal near said locations.

The power spectral densities of the RR intervals and theelectrocardiogram-derived respiratory signal may be calculated using anaveraged periodogram technique.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described with reference to theaccompanying drawings in which:

FIG. 1 is a diagram showing a typical electrocardiogram signal with thevarious intervals and segments identified;

FIG. 2 is a flowchart setting out the steps involved in the method ofthe invention;

FIG. 3 is a schematic diagram of the diagnostic recorder electroniccircuitry for the diagnostic recording apparatus in a first embodiment(PC-configuration embodiment);

FIG. 4 is a diagram of a patient wearing the diagnostic recordingapparatus while sleeping;

FIG. 5 is a schematic diagram of the features of the diagnosticrecording apparatus in the first embodiment (PC-configurationembodiment);

FIG. 6 is a diagram showing the analysis software interface on a PC inaccordance with the first embodiment of the apparatus of the invention;

FIG. 7 is a schematic diagram of the electronic circuitry of thediagnostic recording apparatus in a second embodiment in which the dataacquisition circuitry and analysis software is combined into a singleunit (monitor-configuration embodiment);

FIG. 8 is a perspective view of the apparatus in the second embodimentof the invention (monitor—configuration embodiment); and

FIG. 9 is a schematic diagram of the apparatus in the third embodiment(stand-alone—configuration embodiment).

DETAILED DESCRIPTION

Embodiments of the invention will now be described with reference to thedrawings in which three embodiments are shown.

Referring initially to the flow chart of FIG. 2, the steps involved inthe method of diagnosing sleep apnea in accordance with the presentinvention will now be described

Step 1: Acquire Unprocessed Electrocardiogram Data:

A single channel electrocardiogram signal is acquired from a human usingthe diagnostic recording apparatus of the invention which incorporates apair of differential electrodes. This is followed by amplification ofthe electrocardiogram signal, analogue anti-aliasing filtering, andanalogue-to-digital signal conversion.

Step 2: Electrocardiogram Pre-processing

The unprocessed electrocardiogram signal acquired in Step 1 is corruptedby motion artifact, EMG noise, and powerline noise. Digital filteringtechniques including a notch filter, lowpass filter, and median filterare employed to attenuate the effects of noise. The diagnostic recordingapparatus includes signal processing circuitry to carry out such digitalfiltering techniques.

Step 3: Check if Electrocardiogram Signal is Suitable:

In exceptional circumstances, the filtered signal may still beunsuitable for further processing (e.g., too high a level of noise, orelectrodes not connected). An algorithm such as examining the meanenergy of the signal may be used to determine if a valid signal is beingdetected. If a signal is not being detected, then an error signal willbe displayed on the diagnostic recording apparatus.

Step 4: Display Error if Signal is Deemed Unsuitable at Step 3:

Display error—a clear indicator will be visible (and may be audible) toindicate that the measured signal is not valid.

Step 5: Identify QRS Complexes:

The times of occurrences of the R-wave peak in the QRS complexes aredetermined. This stage of processing gives a series of times at whichthe R waves occur, and is used to determine the RR intervals.

Step 6: Calculate the ECG Derived Respiratory Signal

An electrocardiogram-derived respiratory signal is calculated by takingthe locations of the R-wave peaks, and calculating the area representedby the signal near those locations.

Step 7: Partition ECG into Time Periods

The electrocardiogram is partitioned into a series of time periods. Anexample is the partitioning of the electrocardiogram into time periodsof one minute duration.

Step 8: Calculate Measurements Using RR Interval Power Spectral Density:

The RR interval power spectral density is calculated from the RRintervals, using an averaged periodogram technique over the time periodsof data.

Step 9: Calculate Measurements Using the Electrocardiogram DerivedRespiratory Signal Power Spectral Density:

The power spectral density of the electrocardiogram-derived respiratorysignal is calculated using the averaged periodogram technique over thetime periods of data.

Step 10: Calculate Time Domain Electrocardiogram Measurements from theRR Intervals:

The following set of time domain measurements are calculated over thetime periods of the RR intervals—they are more fully explained later:the mean RR interval, the standard deviation of the RR intervals, thefirst five serial correlation coefficients of the RR intervals, twovariants of the value of NN50, two variants of the value of PNN50, thevalue of SDSD, the value of RMSSD, the value of the Allan factor at fivedifferent time scales, the mean electrocardiogram-derived respiratorysignal amplitude, and the standard deviation of theelectrocardiogram-derived respiratory signal amplitude. Global timedomain measurements are also calculated from the entire recording. Theseare the mean and standard deviation of the RR intervals and theelectrocardiogram-derived respiratory signal

Step 11: Combine Measurements for Each Time Period:

The measurements from the RR interval power spectral density, theelectrocardiogram-derived respiratory power spectral density, and thetime domain electrocardiogram calculations obtained from steps 8, 9, and10 for each time period are combined to be processed by step 11.

Step 12: Calculate Probability of Each Time Period Being Apneic orNormal:

The measurements from step 11 are processed by a classifier that hasbeen trained on a pre-existing database of electrocardiogram signals. Itcalculates two probabilities for each time period. The first is theprobability of one or more apneic episodes having occurred during thattime period. The second is the probability of no apneic episodes havingoccurred during that time period. Note that the sum of the twoprobabilities is always one.

Step 13: Post-processing of Probabilities from Each Time Period:

The probability of each time period is recalculated by averaging it withthe probabilities calculated from adjacent time periods. This introducesa time latency into the system.

Step 14: Classify Time Periods:

Each time period is assigned to either the normal or apneic classaccording to the class which has the higher probability from step 13.

Step 15: Combine Time Period Classifications to Form an OverallDiagnostic Measure:

The overall diagnostic measure is arrived at by combining the results ofthe time period classifications.

Step 16: Display Results of Classification:

The results for each time period and the overall diagnostic measure aredisplayed on a graphical interface, or in text form.

EXAMPLE

The following is a more detailed description of the classificationtechnique used in the invention, given by way of specific example.Electrocardiogram signals are obtained from a patient by usingelectrodes and analogue pre-amplifiers as in any standard Holtermonitor. Suitable analogue amplification and filtering is implemented toprovide a signal which is approximately band limited and within therange of the analog-to-digital converter. Preferably, theelectrocardiogram signal is sampled at a rate of 100 Hz or higher, andat up to 16 bits per sample. The unprocessed signal is passed through avariety of digital filtering stages to provide bandpass filtering andremoval of motion artifacts, powerline noise, and EMG noise.

A software algorithm to perform QRS detection is implemented. Thesoftware algorithm may be implemented on a computer, for example PC at alocation remote from the recording apparatus. Alternatively, thesoftware algorithm for QRS detection may be contained within therecording apparatus itself. This software algorithm provides the timesat which the R-wave maximum occurs. The RR intervals are defined as theinterval between R-wave peaks. This algorithm may provide some incorrectRR intervals. Data pre-processing steps to correct physiologicallyunreasonable RR intervals are carried out as follows:

Suspect RR intervals are found by applying a median filter of width fiveto the sequence of RR intervals. This provides a robust estimate of theexpected value for each RR interval. Variations from this expected valuelead to it being flagged as a suspect interval. Extraneous QRSdetections are found by comparing the sum of adjacent RR intervals withthe robust RR estimate. If this sum is numerically closer to the robustestimate than either of the individual RR intervals then an extraneousdetection is present, and the two RR intervals are merged to form asingle interval. Conversely, if an RR interval is a factor of 1.8 timesor more than the robust estimate then it is probable that a QRS complexwas not detected. To recover the missing QRS complexes the RR intervalis divided by the sequence of integers 2, 3, 4, . . . until it bestmatches the robust estimate of the RR interval. The single RR intervalis then subdivided by the appropriate integer to form a series of newdetections. For each new detection, a search is made in region of 100milliseconds either side of that detection for the maximum of theelectrocardiogram signal. If this maximum is similar to the maxima ofthe surrounding QRS complexes, its time of occurrence is accepted as avalid QRS detection point, otherwise the original new detection point isused.

During the breathing cycle, the body-surface electrocardiogram isinfluenced by the electrode motion relative to the heart and by changesin thoracic electrical impedance as lungs fill and empty with air. Theeffect is most obviously seen as a slow modulation of the QRS amplitudeat the same frequency as the breathing cycle. To access this signal, theunprocessed electrocardiogram signal is processed using a linear phasehigh pass filter with a cut-off frequency of 0.5 Hz to remove baselinewander. At every QRS detection time, a sample point of anelectrocardiogram-derived respiratory signal is defined by calculatingthe area enclosed by the electrocardiogram in the region 50 ms beforethe R wave-maximum to 50 ms after

The processing steps outlined above result in (1) a corrected set of RRintervals and (2) an electrocardiogram-derived respiratory signalderived from the R-wave amplitude for each one-minute time period. Basedon these two signals derived from the electrocardiogram, a set ofmeasurements is extracted which is then used for classifying whether apatient has sleep apnea or not and if so, the degree of sleep apnea.

The following measurements are obtained for each one-minute time period:

a) the power spectral density of the electrocardiogram-derivedrespiratory signal;

b) the mean and standard deviation of the electrocardiogram-derivedrespiratory signal;

c) the power spectral density of the RR-intervals;

d) the mean and standard deviation of the RR signal;

e) the first five serial correlation coefficients of the RR-intervals;

f) the Allan factor evaluated at a time scales of 5, 10, 15, 20 and 25seconds where the Allan factor is defined as${{A(T)} = \frac{E\left\{ \left\lbrack {{N_{i + 1}(T)} - {N_{i}(T)}} \right\rbrack^{2} \right\}}{2E\left\{ {N_{i + 1}(T)} \right\}}},$and N/(T) is the number of R wave peaks occurring in a window of lengthT stretching from iT to (i+1)T;

g) the NN50 measure (variant 1), defined as number of pairs of adjacentRR intervals where the first interval exceeds the second interval bymore than 50 ms;

h) the NN50 measure (variant 2), defined as number of pairs of adjacentRR intervals where the second interval exceeds the first interval bymore than 50 ms;

i) two pNN50 measures, defined as the each NN50 measure divided by thetotal number of RR intervals;

j) the SDSD measures, defined as the standard deviation of thedifferences between adjacent RR intervals, and

k) the RMSSD measure defined as the square root of the mean of the sumof the squares of differences between adjacent RR intervals.

All of the measurements (a)-(k) may be calculated over one minute ofrecording. In addition, four measurements are included for each minuteby calculating the mean and standard deviation of the RR signal over theentire recorded electrocardiogram as well as the mean and standarddeviation of the electrocardiogram-derived respiratory signal over theentire recorded electrocardiogram. Hence these four measurements are thesame for all one-minute time periods of a recording.

The RR interval based power spectral density estimate is calculated inthe following way. A sequence of RR intervals is associated with eachone-minute time period. The index for this sequence is beat number, nottime. The mean RR interval for that time period is removed from eachvalue, to yield a zero-mean sequence. The sequence is zero-padded tolength 256, and the fast Fourier transform is taken of the entiresequence. The squared amplitude of these transform values yield aperiodogram estimate of the power spectral density, which has a highvariance. Averaging of four adjacent frequency bins is used to yield a64-point RR interval based power spectral density estimate (of whichonly bins 0-32 are relevant since bins 33-63 contain redundantmeasurements). The x-axis has units of cycles/beat (not Hz as for arate-based power spectral density.

The electrocardiogram-derived respiratory spectrum is calculated asfollows. For each record, the sequence of R-wave areas are calculatedand a discrete sequence formed for each one-minute time period. The meanvalue for the block is removed prior to spectral estimation using theperiodogram technique outlined above.

For each minute of recorded electrocardiogram, therefore an 88-elementmeasurement vector x is created; vector x is composed of 32 separatefrequency bins from the electrocardiogram-derived respiratory spectrum,and 32 separate frequency bins from the RR spectrum and the othermeasurements described above. This measurement vector is then used toclassify the measured data into one of two classes: normal or apnea,using a linear discriminant classifier. In such a classifier, eachmeasurement vector generates a set of K probability measures, where K isthe number of possible classes (two in this case i.e. normal or apnea).The measure used is:${y_{k} = {{{- \frac{1}{2}}\mu_{k}^{T}{\sum\limits^{- 1}\mu_{k}}} + {{- \frac{1}{2}}\mu_{k}^{T}{\sum\limits^{- 1}x}} + {\log\left( {p(k)} \right)}}},{k = 1},2,$where k denotes the class number, μ_(k) is the class mean, Σ is thecommon covariance matrix, and p(k) is the a priori probability for thatclass. The class k which produces the highest value of y_(k) is chosenas the correct class. The parameters μ_(k) and Σ are chosen by aclassifier training technique which is fully described in B. D. Ripley,“Pattern Recognition and Neural Networks” Cambridge University Press1996. The linear discriminant classifier used in this disclosure wastrained on a set of 35 records, each consisting of approximately 7-10hours of electrocardiogram data. This equates to approximately 16000minutes which have been classified as being either normal or apnea by anindependent human expert. This classification is described in thePhilipps University Sleep Apnea electrocardiogram database, available athttp://physionet.org.

In addition, the classifications of each one-minute time period are notindependent. It is possible to predict the classification of aone-minute time period based only on the classifications of thesurrounding epochs with a success rate higher than that of randomguessing. In light of this observation, the system and method of thepresent invention includes a technique to boost classificationperformance. To achieve this, the posterior probabilities of a timeperiod are averaged with the posterior probabilities of the surroundingtime periods. Another technique that may be used to boost classificationperformance is to average the measurement values over adjacent timeperiods before the classification stage.

Since the linear discriminant classifier provides a numerical estimateof the probability of apnea being present during a given one-minute timeperiod, this knowledge can be used to assign a statistical confidencerating to each time period classification.

An unbiased estimate of expected classification performance using anadditional 35 recordings not used in the training process says that oursystem will correctly classify 90.6% of one minute time periods. Factorswhich contribute to loss of accuracy are errors in the database“gold-standard” marking, data acquisition noise, and limitations in howwell the measured data can actually capture the presence or absence ofsleep apnea. As a comparative figure, it is estimated a highly skilledhuman observer using a polysomnogram would achieve an accuracy of around93% on a per-minute basis.

The overall patient diagnosis is arrived at by combining the results ofthe minute-by-minute time periods. For example, if a person is found tohave greater than 20% of their time being classified as apnea, then theoverall record is denoted as apnea. Since the overall diagnosis combinesinformation from many one-minute time periods, its accuracy is higher.On an independent data set of 30 clearly diagnosed patients available tothe applicants, the diagnosis reached by using the apparatus, system,and method of the present invention, was in agreement with the clinicianin all 30 cases.

There are three embodiments of the apparatus, system and method ofdiagnosing sleep apnea in accordance with this disclosure. These willnow be described in turn.

Referring to FIGS. 3 to 6, the apparatus in the first embodiment,referred to as the PC-configuration, will be described. The apparatus inthis first embodiment is indicated generally in FIG. 5 by the referencenumeral 10. The apparatus 10 comprises a low noise amplifier, analoguefiltering, analogue-to-digital conversion, digital filtering, digitalmemory including EPROM, random access memory (RAM), a microcontroller orDSP processor, and a high speed PC serial interface as indicated in FIG.3. The apparatus also includes electrode inputs 11, a power indicator 12and a PC interface port 13. The overall functionality of the diagnosticrecording apparatus 10 is to provide a clean electrocardiogram signalthat can be analyzed using the method of the invention described above.

The diagnostic recording apparatus is configured to accept differentialinputs from several electrodes, giving one or more channels ofelectrocardiogram recording. The low noise amplifier provides initialamplification of the electrocardiogram signal. The anti-aliasing filteris required to allow digital sampling later in the circuit. This isfollowed by further amplification, and analogue-to-digital conversion.The controlling unit in the diagnostic recording apparatus is amicrocontroller or DSP processor. The DSP processor may implementdigital filtering to improve signal quality. The diagnostic recordingapparatus 10 includes an EPROM to store its programming, random accessmemory (RAM) for intermediate calculations, and external flash memory tostore the digital electrocardiogram data. The apparatus 10 also has aserial interface (such as a USB bus) that will allow it to communicatewith an external computer since the analysis of the electrocardiogramrecorded by the apparatus 10 is carried out on a computer at a locationremote from apparatus 10.

The diagnostic recording apparatus may have the capability to record oneor more channels of electrocardiogram signal. The recorder will store upto 24 hours of one or more channel recordings in a digital format usingthe flash memory indicated in the electronic circuitry diagram shown inFIG. 3. The diagnostic recording apparatus is lightweight, small insize, and battery powered and is rugged and light enough to be used onan outpatient basis (i.e., ambulatory).

FIG. 4 shows how a patient can sleep while recording is occurring, withthe recorder attached to the patient by means of a belt.

FIG. 5 shows a sketch of the physical appearance of the diagnosticrecording apparatus. The diagnostic recording apparatus 10 is small,measuring approximately 12 cm.times.8 cm and weighing about 150 g. Theanalysis carried out when implementing the method of this disclosure,described herein above as being implemented on a personal computer,which will also provide auxiliary functionality such as data archiving,database management, and compression.

An embodiment of the analysis software interface is shown in FIG. 6. Theinterface details in graphic and text form the minute-by-minutebreakdown of the electrocardiogram recording and apnea classification.It can use a simple color coding scheme (i.e. green for normal, red forapnea, orange for borderline) to provide an easy visual summary of theapnea activity. The interface can provide information such as the totalnumber of apneic minutes, the percentage of apneic minutes, and anestimate of the apnea-hypopnea index (AHI).

The diagnostic recording apparatus 10 in this first embodiment is idealfor home monitoring, where the function of recording is separated fromthe function of analysis. The patient can be issued with the diagnosticrecording apparatus, electrodes can be put in place, and the patient canreturn home to sleep. The following day (or at their own convenience),the patient can return the diagnostic recorder to the site where it wasissued. Alternatively, in this embodiment, there may be a data linkbetween the diagnostic recording apparatus 10 and the analysis system.The data acquired by the diagnostic recording apparatus may then be sentvia a network to a separate physical location, and the analysis carriedout at the remote location. This could be achieved by incorporating thefunctionality of a modem or wireless phone into the diagnostic recordingapparatus, so that it can communicate with a computer located at theremote location. This has the advantage of providing a fasterturn-around in analysis time.

Referring now to FIGS. 7 and 8, the second embodiment of the diagnosticrecording apparatus (referred to as monitor-configuration) is indicatedgenerally by reference numeral 20 and this embodiment will now bedescribed. The apparatus 20 incorporates the data acquisition circuitryincluding the low noise amplifier, analogue filtering,analogue-to-digital conversion, digital filtering, as well as theanalysis software into a single physical unit. The diagnostic recordingapparatus 20 also incorporates a user interface allowing the user toview the results of the analysis. The electrocardiogram electrodes 25will be plugged directly into the diagnostic recording apparatus 20. Thediagnostic recording apparatus 20 also contains a user display (labeledDISPLAY SCREEN in FIG. 7), and a simple user interface (i.e., simpleback-forward buttons, labeled USER CONTROL in FIG. 7). The apparatus 20could be used as a general-purpose monitoring unit to which other dataacquisition cards may be attached (e.g., circuitry to measure nasalairflow, SaO.sub.2, etc.). The diagnostic recording apparatus 20 ispowered using mains supply. The apparatus 20 in this second embodimentis not intended to be ambulatory but rather is ideal for the situationwhere a subject is in a sleep laboratory or other medical facility.

Referring now to FIG. 9, the diagnostic recording apparatus in a thirdembodiment (referred to as stand-alone-configuration) will be described.The apparatus in this embodiment is indicated generally by referencenumeral 30. The apparatus 30 incorporates both the data acquisition andanalysis software into a complete self-contained portable device. Theapparatus 30 provides low noise amplification, analogue filtering,analogue-to-digital conversion, digital filtering, as well as providingthe analysis. The apparatus 30 includes a simple user display (e.g. asmall LCD screen), electrode inputs 31, power indicator 32 and serialinterface 33. The device requires low-power, is low in weight, andbattery operated and is also small (measuring approximately 12cm.times.8 cm and weighing approximately 150 g). The apparatus 30 issuitable as a home healthcare consumer product, and can provide asummary of the night's sleep directly as output to a user. This might beideal for patients who have undergone some treatment for sleep apnea,and who wish to monitor their progress.

In each of the above embodiments, measurements of additionalphysiological parameters such as SaO.sub.2 level, respiratory effort,nasal airflow, body movement, etc. may be incorporated to enhanceclassification.

An advantage of the apparatus, system and method of this disclosure overconventional approaches in which 12 or more signals are often recorded,is that in the above-described simpler system, only one or two signalswill be recorded using up to perhaps three electrical leads. This meansthat there are fewer body electrodes and measurement devices required.Furthermore, patient acceptance of skin electrodes on the chest is good,since such electrodes are comfortable, familiar and unobtrusive tosleeping for the general public.

The apparatus, method and system of this disclosure have the advantagethat they provide diagnostic accuracy comparable with a completepolysomnogram, but at significantly reduced cost. The apparatus can alsobe embodied in a form suitable for home use, which lowers cost, andincreases patient acceptance.

The apparatus, method and system of diagnosing sleep apnea in accordancewith this disclosure has several advantages over conventionalapproaches. These at least include the following:

The apparatus and method rely upon use of the electrocardiogram signalonly, rather than on the combination of electrocardiogram measurementswith other signals such as SaO₂, sound, bodily position, etc.

The apparatus and method may incorporate a step to isolate anelectrocardiogram-derived respiratory signal, which has usefuldiagnostic information. This has not previously been used to diagnosesleep apnea.

The apparatus and method may incorporate the use of spectral measuresderived from the RR intervals and electrocardiogram-derived respiratorysignal which have not been previously used to diagnose sleep apnea.

The apparatus and method may incorporate a large range of bothtime-domain and frequency-domain statistical measures of RR intervalswhich have not previously been used to diagnose sleep apnea

The apparatus and method may incorporate post-processing of the timeperiod classifications which increases the accuracy.

The apparatus and method may provide a diagnostic measure of sleep apneafor each time period. This is as compared to the Apnea-Hypopnea Index orRespiratory Disturbance Index currently used.

A further advantage is that the diagnostic recording apparatus can bemade small and light enough to be worn comfortably while sleeping, andwith sufficiently low power consumption to be powered by simplecommercially available batteries.

A further advantage is that the device can be made small enough and easyto use at home allowing a simple screening protocol for sleep apnea tobe implemented.

A further advantage is that the ambulatory diagnostic recordingapparatus and diagnostic interpretation can be at two physically remotelocations, allowing for remote monitoring and interpretation.

A further advantage of the system is that since the diagnostic recordingapparatus records the electrocardiogram, it can provide usefulinformation to other diagnostic systems, such as those monitoring forcardiac arrhythmias.

Thus, the apparatus, system and method disclosed above provides aninexpensive, highly-automated, diagnostic evaluation that has proven tobe effective in classifying records from a known database ofelectrocardiogram signals. Tests on independent records show that thediagnostic accuracy on a per-minute basis is 90.6%, and on a per-patientbasis is 100%.

It is to be understood that this disclosure has been particularlydescribed with reference to single channel electrocardiograms,multi-channel electrocardiograms may equally be used in the apparatus,system and method of this disclosure.

It is thought that the various embodiments of the invention and itsadvantages will be understood from the foregoing description and it willbe apparent that various changes may be made thereto without departingfrom the spirit and scope of the invention, the forms hereinbeforedescribed being merely preferred or exemplary embodiments thereof.

1. An apparatus useful in diagnosing sleep apnea in a human patientusing only an electrocardiogram signal relating to the patient, theapparatus comprising: a computer circuit configured to: analyze theelectrocardiogram signal; classify each time period in a set of timeperiods of the electrocardiogram signal as either apneic or normal; andprovide a diagnostic measure of sleep apnea for the human patient basedon classification results obtained by combining a plurality of resultsfrom the set of time periods.
 2. The apparatus of claim 1, wherein thecomputer circuit is configured to analyze the electrocardiogram signalby determining a plurality of R-R intervals in the electrocardiogramsignal.
 3. The apparatus of claim 2, wherein the computer circuit isconfigured to classify a time period in the set of time periods aseither apneic or normal by using a power spectral density of anassociated RR interval.
 4. The apparatus of claim 2, wherein thecomputer circuit is configured to provide a diagnostic measure of sleepapnea for the human patient by deriving a respiratory signal from theelectrocardiogram signal.
 5. The apparatus of claim 1, wherein thecomputer circuit is configured to analyze the electrocardiogram signalby deriving a respiratory signal from the electrocardiogram signal. 6.The apparatus of claim 5, wherein the computer circuit is configured toanalyze the electrocardiogram signal by calculating a power spectraldensity of the electrocardiogram-derived respiratory signal.
 7. Theapparatus of claim 1, wherein the computer circuit is configured toanalyze the electrocardiogram signal by using time domain processing. 8.The apparatus of claim 1, wherein the computer circuit is configured toanalyze the electrocardiogram signal by using frequency domainprocessing.
 9. The apparatus of claim 1, wherein the computer circuit isconfigured to analyze the electrocardiogram signal by using both timedomain and frequency domain processing.
 10. The apparatus of claim 1,wherein the computer circuit is configured to analyze theelectrocardiogram signal by determining a power spectral density duringeach time period of the set of time intervals.
 11. The apparatus ofclaim 1, wherein the computer circuit is configured to calculate, foreach time period in the set of times periods, an associated probabilitythat each time period is apneic.
 12. A computer-readable medium havingcomputer code embodied thereon which, when executed, is useful indiagnosing sleep apnea in a human patient by causing the computer to:analyze an electrocardiogram signal relating to the human patient;classify each time period in a set of time periods of theelectrocardiogram signal as either apneic or normal; and provide adiagnostic measure of sleep apnea for the human patient based onclassification results obtained by combining a plurality of results fromthe set of time periods.
 13. The computer-readable medium of claim 12,wherein the computer code causes the computer to determine a pluralityof R-R intervals in the electrocardiogram signal.
 14. Thecomputer-readable medium of claim 12, wherein the computer code causesthe computer to calculate a power spectral density of an RR interval.15. The computer-readable medium of claim 12, wherein the computer codecauses the computer to derive a respiratory signal from theelectrocardiogram signal.
 16. The computer-readable medium of claim 15,wherein the computer code causes the computer to calculate a powerspectral density of the electrocardiogram-derived respiratory signal.17. The computer-readable medium of claim 12, wherein the computer codecauses the computer to use time domain processing to provide thediagnostic measure of sleep apnea.
 18. The computer-readable medium ofclaim 12, wherein the computer code causes the computer to use frequencydomain processing to provide the diagnostic measure of sleep apnea. 19.The computer-readable medium of claim 12, wherein the computer codecauses the computer to use both time domain and frequency domainprocessing to provide the diagnostic measure of sleep apnea.
 20. Thecomputer-readable medium of claim 12, wherein the computer code causesthe computer to calculate, for each time period in the set of timesperiods, an associated probability that each time period is apneic.